Prediction of Longshore Sediment Transport Using Soft Computing Techniques

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Human thermo-physiology models (HTPM) are useful tools to assess dynamic and non-uniform human thermal states. However, they are developed based on the physiological data of an average person. In this paper, we present a detailed evaluation of two sophisti ...

The ecological effects of dams on sediment and river flow have been subject to an increasing attention, leading to the implementation of mitigation measures such as environmental flow release and sediment replenishment. However, fine sediment dynamics have ...

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise. Real-time non-invasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration ...

We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio R between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, and the decay rate of gravitational ...

Prediction of Longshore Sediment Transport Using Soft Computing Techniques

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Timing and spatial selection bias in rapid extreme event attribution

Iron Reduction in Profundal Sediments of Ultraoligotrophic Lake Tahoe under Oxygen-Limited Conditions

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Continuous monitoring of morphological changes from sediment augmentation by field measurements and flume experiments

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Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

An Unbiased Method of Measuring the Ratio of Two Data Sets